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Forecasting brazilian ethanol spot prices using lstm

Energies, ISSN: 1996-1073, Vol: 14, Issue: 23
2021
  • 4
    Citations
  • 0
    Usage
  • 18
    Captures
  • 1
    Mentions
  • 73
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    4
    • Citation Indexes
      3
    • Policy Citations
      1
      • 1
  • Captures
    18
  • Mentions
    1
    • Blog Mentions
      1
      • Blog
        1
  • Social Media
    73
    • Shares, Likes & Comments
      73
      • Facebook
        73

Article Description

Ethanol is one of the most used fuels in Brazil, which is the second-largest producer of this biofuel in the world. The uncertainty of price direction in the future increases the risk for agents operating in this market and can affect a dependent price chain, such as food and gasoline. This paper uses the architecture of recurrent neural networks—Long short-term memory (LSTM)—to predict Brazilian ethanol spot prices for three horizon-times (12, 6 and 3 months ahead). The proposed model is compared to three benchmark algorithms: Random Forest, SVM Linear and RBF. We evaluate statistical measures such as MSE (Mean Squared Error), MAPE (Mean Absolute Percentage Error), and accuracy to assess the algorithm robustness. Our findings suggest LSTM outperforms the other techniques in regression, considering both MSE and MAPE but SVM Linear is better to identify price trends. Concerning predictions per se, all errors increase during the pandemic period, reinforcing the challenge to identify patterns in crisis scenarios.

Bibliographic Details

Gustavo Carvalho Santos; Antônio Cláudio Paschoarelli Veiga; Flavio Barboza; Mateus Ferreira Silva

MDPI AG

Energy; Mathematics; Engineering

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